Artificial intelligence and anti-cancer drugs: advancing discovery, personalization, and precision oncology
- 1College of Biotechnology, U.P. Pt. Deen Dayal Upadhyaya Pashu Chikitsa Vigyan Vishwavidyalaya Evam Go Anusandhan, Sansthan (DUVASU)
- 2Amity Institute of Biotechnology, Amity University Uttar Pradesh
Abstract
Artificial intelligence (AI) is changing the way we discover anticancer drugs. It tackles the problems of traditional methods, such as prolonged timelines for drug development, higher costs, and substantial inter-patient heterogeneity. This review explains how AI is applied throughout the cancer drug pipeline. This includes target identification, virtual screening of compounds, molecular docking, and predicting pharmacokinetics. We explore how machine learning models speed up hit-to-lead optimization, improve clinical trial design by stratifying patients, and allow for real-time toxicity monitoring. A key focus is on integrating AI with multi-omics data, including proteomics, genomics, transcriptomics, and metabolomics, to support precision oncology and personalized treatments. We address important validation challenges, like model interpretability, data quality, and regulatory issues that must be solved before AI can be widely used in clinics. By summarizing current evidence and targeting research gaps, this review offers a clear view of how AI is enhancing the discovery, personalization, and validation of anticancer treatments.
Related
- Editorial: Artificial intelligence and anti-cancer drugs: advancing discovery, personalization, and precision oncology
- Artificial Intelligence-driven Biomarker Discovery for Precision Psychiatry
- Advancing Genitourinary Cancer Surgery: The Role of Artificial Intelligence and Robotics
- Toward Precision Colorectal Cancer Screening: Integrating Clinical Risk Stratification, Molecular Biomarkers, and Artificial Intelligence
- Artificial Intelligence in Oncologic Imaging: Deep Learning, Radiomics, and Clinical Integration for Precision Cancer Diagnosis
BibTeX
@article{nagrami2026artificial,
title = {{Artificial intelligence and anti-cancer drugs: advancing discovery, personalization, and precision oncology}},
author = {Faizan ul Haque Nagrami and Pahchan Shukla and Ruchi Yadav and Jyoti Prakash},
journal = {Frontiers in Oncology},
year = {2026},
month = oct,
doi = {10.3389/fonc.2026.1885312},
url = {https://www.frontiersin.org/articles/10.3389/fonc.2026.1885312/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Artificial intelligence and anti-cancer drugs: advancing discovery, personalization, and precision oncology
AU - Faizan ul Haque Nagrami
AU - Pahchan Shukla
AU - Ruchi Yadav
AU - Jyoti Prakash
PY - 2026
DA - 2026/10/13
JO - Frontiers in Oncology
DO - 10.3389/fonc.2026.1885312
UR - https://www.frontiersin.org/articles/10.3389/fonc.2026.1885312/full
AB - Artificial intelligence (AI) is changing the way we discover anticancer drugs. It tackles the problems of traditional methods, such as prolonged timelines for drug development, higher costs, and substantial inter-patient heterogeneity. This review explains how AI is applied throughout the cancer drug pipeline. This includes target identification, virtual screening of compounds, molecular docking, and predicting pharmacokinetics. We explore how machine learning models speed up hit-to-lead optimization, improve clinical trial design by stratifying patients, and allow for real-time toxicity monitoring. A key focus is on integrating AI with multi-omics data, including proteomics, genomics, transcriptomics, and metabolomics, to support precision oncology and personalized treatments. We address important validation challenges, like model interpretability, data quality, and regulatory issues that must be solved before AI can be widely used in clinics. By summarizing current evidence and targeting research gaps, this review offers a clear view of how AI is enhancing the discovery, personalization, and validation of anticancer treatments.
SN - 2234-943X
ER -